A comparative study of ensemble learning methods for classification in bioinformatics

Verma, Aayushi; Mehta, Shikha

doi:10.1109/confluence.2017.7943141

Cited by 37 publications

(12 citation statements)

References 9 publications

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“…AdaBoost (Adaptive Boosting) is an ensemble classifier that produces accurate prediction rules via combining several weak learners into a weighted majority hypothesis, adaptively changing weights based on the accuracies of individual components (Freund et al, 1999;Freund and Schapire, 1997). This model is widely used in bioinformatics applications (Niu et al, 2006;Olson et al, 2018;Verma and Mehta, 2017;Xie et al, 2006;Yang et al, 2010;Zhong et al, 2013). The algorithm has an iterative nature, updating weight vector W = w 1 ; .…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

2021

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Section: Machine Learning Approachesmentioning

confidence: 99%

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

2021

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“…By using multiple learners, the combined ability of the ensemble can be greater than a single learner [35]. Ensemble learning has been applied in many fields, such as bioinformatics [36], finance [37], and healthcare [38]. Ensemble learning applied to sentiment analysis has demonstrated advantages for many classification problems as shown by recent studies [39]- [40].…”

Section: Ensemble Learningmentioning

confidence: 99%

Capturing Contextual Factors in Sentiment Classification: An Ensemble Approach

Tran

Phan

2020

IEEE Access

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Sentiment classification is a crucial task in sentiment analysis, and has received significant attention from researchers. Previous studies have focused on using several techniques to solve this problem. However, to the best of our knowledge, none of these works has fully investigated the exploitation and the manipulation of contextual information in the text, or taken advantage of the combined power of state-of-theart models. In this paper, we propose an effective ensemble learning model for the sentiment classification problem. In our system, the contextual information in the text is fully captured by integrating rule-based methods and other state-of-the-art deep learning models. We found that the combination of word embedding representation and the attention mechanism, along with pre-defined rules and specific-domain sentiment dictionaries are helpful in dealing with numerous valence-shifting cases. Although the computational cost of the proposed system is higher than those of certain other algorithms, this system obtains better results than other approaches when tested on three different datasets.

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“…The idea of a stacking based machine learning technique [72] which has recently been successfully applied to solve some interesting bioinformatics problems [26,33,[73][74][75] is utilized in this work to develop the StackCBPred predictor for carbohydrate-binding sites prediction.…”

Section: Chapter 5 Stackingmentioning

confidence: 99%

StackCBPred: A stacking based prediction of protein-carbohydrate binding sites from sequence

Gattani

Mishra

Hoque

2019

Carbohydrate Research

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A comparative study of ensemble learning methods for classification in bioinformatics

Cited by 37 publications

References 9 publications

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Capturing Contextual Factors in Sentiment Classification: An Ensemble Approach

StackCBPred: A stacking based prediction of protein-carbohydrate binding sites from sequence

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